The Matchmaker Exchange: a platform for rare disease gene discovery

WDR44 Loss-of-Function Promoter Deletion in a Male Newborn With a Ciliopathy Phenotype

Gain-of-function variants in the WDR44 gene have recently been associated with an X-linked ciliopathy-related neurodevelopmental phenotype. Here, we report on a WDR44 loss-of-function (LOF) variant identified in the genome sequence from a male fetus enrolled in the Prenatal Genetic Diagnosis by Genomic Sequencing (PrenatalSEQ) multicenter study. The phenotype is consistent with the described X-linked ciliopathy that includes developmental delay, microcephaly, congenital heart defects, kidney abnormalities, cryptorchidism, musculoskeletal abnormalities, craniofacial dysmorphism, and effusions. This is the first report of a WDR44 LOF variant in an affected individual with a prenatal presentation and supports LOF as a mechanism for the X-linked WDR44 ciliopathy-related phenotype.

Promoting equity in polygenic risk assessment through global collaboration

The long delay before genomic technologies become available in low- and middle-income countries is a concern from both scientific and ethical standpoints. Polygenic risk scores (PRSs), a relatively recent advance in genomics, could have a substantial impact on promoting health by improving disease risk prediction and guiding preventive strategies. However, clinical use of PRSs in their current forms might widen global health disparities, as their portability to diverse groups is limited. This Perspective highlights the need for global collaboration to develop and implement PRSs that perform equitably across the world. Such collaboration requires capacity building and the generation of new data in low-resource settings, the sharing of harmonized genotype and phenotype data securely across borders, novel population genetics and statistical methods to improve PRS performance, and thoughtful clinical implementation in diverse settings. All this needs to occur while considering the ethical, legal and social implications, with support from regulatory and funding agencies and policymakers.

Congenital anterior segment ocular disorders: Genotype-phenotype correlations and emerging novel mechanisms

Development of the anterior segment of the eye requires reciprocal sequential interactions between the arising tissues, facilitated by numerous genetic factors. Disruption of any of these processes results in congenital anomalies in the affected tissue(s) leading to anterior segment disorders (ASD) including aniridia, Axenfeld-Rieger anomaly, congenital corneal opacities (Peters anomaly, cornea plana, congenital primary aphakia), and primary congenital glaucoma. Current understanding of the genetic factors involved in ASD remains incomplete, with approximately 50% overall receiving a genetic diagnosis. While some genes are strongly associated with a specific clinical diagnosis, the majority of known factors are linked with highly variable phenotypic presentations, with pathogenic variants in FOXC1, CYP1B1, and PITX2 associated with the broadest spectrum of ASD conditions. This review discusses typical clinical presentations including associated systemic features of various forms of ASD; the latest functional data and genotype-phenotype correlations related to 25 ASD factors including newly identified genes; promising novel candidates; and current and emerging treatments for these complex conditions. Recent developments of interest in the genetics of ASD include identification of phenotypic expansions for several factors, discovery of multiple modes of inheritance for some genes, and novel mechanisms including a growing number of non-coding variants and alleles affecting specific domains/residues and requiring further studies.

Privacy-Enhancing Technologies in Biomedical Data Science

The rapidly growing scale and variety of biomedical data repositories raise important privacy concerns. Conventional frameworks for collecting and sharing human subject data offer limited privacy protection, often necessitating the creation of data silos. Privacy-enhancing technologies (PETs) promise to safeguard these data and broaden their usage by providing means to share and analyze sensitive data while protecting privacy. Here, we review prominent PETs and illustrate their role in advancing biomedicine. We describe key use cases of PETs and their latest technical advances and highlight recent applications of PETs in a range of biomedical domains. We conclude by discussing outstanding challenges and social considerations that need to be addressed to facilitate a broader adoption of PETs in biomedical data science.

Next-generation phenotyping integrated in a national framework for patients with ultrarare disorders improves genetic diagnostics and yields new molecular findings

Individuals with ultrarare disorders pose a structural challenge for healthcare systems since expert clinical knowledge is required to establish diagnoses. In TRANSLATE NAMSE, a 3-year prospective study, we evaluated a novel diagnostic concept based on multidisciplinary expertise in Germany. Here we present the systematic investigation of the phenotypic and molecular genetic data of 1,577 patients who had undergone exome sequencing and were partially analyzed with next-generation phenotyping approaches. Molecular genetic diagnoses were established in 32% of the patients totaling 370 distinct molecular genetic causes, most with prevalence below 1:50,000. During the diagnostic process, 34 novel and 23 candidate genotype-phenotype associations were identified, mainly in individuals with neurodevelopmental disorders. Sequencing data of the subcohort that consented to computer-assisted analysis of their facial images with GestaltMatcher could be prioritized more efficiently compared with approaches based solely on clinical features and molecular scores. Our study demonstrates the synergy of using next-generation sequencing and phenotyping for diagnosing ultrarare diseases in routine healthcare and discovering novel etiologies by multidisciplinary teams.

The phenotypic and genotypic spectrum of individuals with mono- or biallelic ANK3 variants

ANK3 encodes ankyrin-G, a protein involved in neuronal development and signaling. Alternative splicing gives rise to three ankyrin-G isoforms comprising different domains with distinct expression patterns. Mono- or biallelic ANK3 variants are associated with non-specific syndromic intellectual disability in 14 individuals (seven with monoallelic and seven with biallelic variants). In this study, we describe the clinical features of 13 additional individuals and review the data on a total of 27 individuals (16 individuals with monoallelic and 11 with biallelic ANK3 variants) and demonstrate that the phenotype for biallelic variants is more severe. The phenotypic features include language delay (92%), autism spectrum disorder (76%), intellectual disability (78%), hypotonia (65%), motor delay (68%), attention deficit disorder (ADD) or attention deficit hyperactivity disorder (ADHD) (57%), sleep disturbances (50%), aggressivity/self-injury (37.5%), and epilepsy (35%). A notable phenotypic difference was presence of ataxia in three individuals with biallelic variants, but in none of the individuals with monoallelic variants. While the majority of the monoallelic variants are predicted to result in a truncated protein, biallelic variants are almost exclusively missense. Moreover, mono- and biallelic variants appear to be localized differently across the three different ankyrin-G isoforms, suggesting isoform-specific pathological mechanisms.

Considerations for reporting variants in novel candidate genes identified during clinical genomic testing

Since the first novel gene discovery for a Mendelian condition was made via exome sequencing, the rapid increase in the number of genes known to underlie Mendelian conditions coupled with the adoption of exome (and more recently, genome) sequencing by diagnostic testing labs has changed the landscape of genomic testing for rare diseases. Specifically, many individuals suspected to have a Mendelian condition are now routinely offered clinical ES. This commonly results in a precise genetic diagnosis but frequently overlooks the identification of novel candidate genes. Such candidates are also less likely to be identified in the absence of large-scale gene discovery research programs. Accordingly, clinical laboratories have both the opportunity, and some might argue a responsibility, to contribute to novel gene discovery, which should, in turn, increase the diagnostic yield for many conditions. However, clinical diagnostic laboratories must necessarily balance priorities for throughput, turnaround time, cost efficiency, clinician preferences, and regulatory constraints and often do not have the infrastructure or resources to effectively participate in either clinical translational or basic genome science research efforts. For these and other reasons, many laboratories have historically refrained from broadly sharing potentially pathogenic variants in novel genes via networks such as Matchmaker Exchange, much less reporting such results to ordering providers. Efforts to report such results are further complicated by a lack of guidelines for clinical reporting and interpretation of variants in novel candidate genes. Nevertheless, there are myriad benefits for many stakeholders, including patients/families, clinicians, and researchers, if clinical laboratories systematically and routinely identify, share, and report novel candidate genes. To facilitate this change in practice, we developed criteria for triaging, sharing, and reporting novel candidate genes that are most likely to be promptly validated as underlying a Mendelian condition and translated to use in clinical settings.

Considerations for reporting variants in novel candidate genes identified during clinical genomic testing

Since the first novel gene discovery for a Mendelian condition was made via exome sequencing (ES), the rapid increase in the number of genes known to underlie Mendelian conditions coupled with the adoption of exome (and more recently, genome) sequencing by diagnostic testing labs has changed the landscape of genomic testing for rare disease. Specifically, many individuals suspected to have a Mendelian condition are now routinely offered clinical ES. This commonly results in a precise genetic diagnosis but frequently overlooks the identification of novel candidate genes. Such candidates are also less likely to be identified in the absence of large-scale gene discovery research programs. Accordingly, clinical laboratories have both the opportunity, and some might argue a responsibility, to contribute to novel gene discovery which should in turn increase the diagnostic yield for many conditions. However, clinical diagnostic laboratories must necessarily balance priorities for throughput, turnaround time, cost efficiency, clinician preferences, and regulatory constraints, and often do not have the infrastructure or resources to effectively participate in either clinical translational or basic genome science research efforts. For these and other reasons, many laboratories have historically refrained from broadly sharing potentially pathogenic variants in novel genes via networks like Matchmaker Exchange, much less reporting such results to ordering providers. Efforts to report such results are further complicated by a lack of guidelines for clinical reporting and interpretation of variants in novel candidate genes. Nevertheless, there are myriad benefits for many stakeholders, including patients/families, clinicians, researchers, if clinical laboratories systematically and routinely identify, share, and report novel candidate genes. To facilitate this change in practice, we developed criteria for triaging, sharing, and reporting novel candidate genes that are most likely to be promptly validated as underlying a Mendelian condition and translated to use in clinical settings.

CIAO1 loss of function causes a neuromuscular disorder with compromise of nucleocytoplasmic Fe-S enzymes

Cytoplasmic and nuclear iron-sulfur (Fe-S) enzymes that are essential for genome maintenance and replication depend on the cytoplasmic Fe-S assembly (CIA) machinery for cluster acquisition. The core of the CIA machinery consists of a complex of CIAO1, MMS19 and FAM96B. The physiological consequences of loss of function in the components of the CIA pathway have thus far remained uncharacterized. Our study revealed that patients with biallelic loss of function in CIAO1 developed proximal and axial muscle weakness, fluctuating creatine kinase elevation, and respiratory insufficiency. In addition, they presented with CNS symptoms including learning difficulties and neurobehavioral comorbidities, along with iron deposition in deep brain nuclei, mild normocytic to macrocytic anemia, and gastrointestinal symptoms. Mutational analysis revealed reduced stability of the variants compared with WT CIAO1. Functional assays demonstrated failure of the variants identified in patients to recruit Fe-S recipient proteins, resulting in compromised activities of DNA helicases, polymerases, and repair enzymes that rely on the CIA complex to acquire their Fe-S cofactors. Lentivirus-mediated restoration of CIAO1 expression reversed all patient-derived cellular abnormalities. Our study identifies CIAO1 as a human disease gene and provides insights into the broader implications of the cytosolic Fe-S assembly pathway in human health and disease.

Biallelic variants in CSMD1 are implicated in a neurodevelopmental disorder with intellectual disability and variable cortical malformations

CSMD1 (Cub and Sushi Multiple Domains 1) is a well-recognized regulator of the complement cascade, an important component of the innate immune response. CSMD1 is highly expressed in the central nervous system (CNS) where emergent functions of the complement pathway modulate neural development and synaptic activity. While a genetic risk factor for neuropsychiatric disorders, the role of CSMD1 in neurodevelopmental disorders is unclear. Through international variant sharing, we identified inherited biallelic CSMD1 variants in eight individuals from six families of diverse ancestry who present with global developmental delay, intellectual disability, microcephaly, and polymicrogyria. We modeled CSMD1 loss-of-function (LOF) pathogenesis in early-stage forebrain organoids differentiated from CSMD1 knockout human embryonic stem cells (hESCs). We show that CSMD1 is necessary for neuroepithelial cytoarchitecture and synchronous differentiation. In summary, we identified a critical role for CSMD1 in brain development and biallelic CSMD1 variants as the molecular basis of a previously undefined neurodevelopmental disorder.

GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases

The most important factor that complicates the work of dysmorphologists is the significant phenotypic variability of the human face. Next-Generation Phenotyping (NGP) tools that assist clinicians with recognizing characteristic syndromic patterns are particularly challenged when confronted with patients from populations different from their training data. To that end, we systematically analyzed the impact of genetic ancestry on facial dysmorphism. For that purpose, we established the GestaltMatcher Database (GMDB) as a reference dataset for medical images of patients with rare genetic disorders from around the world. We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders. Although the predominant ancestry is still European (67%), data from underrepresented populations have been increased considerably via global collaborations (19% Asian and 7% African). This includes previously unpublished reports for more than 40% of the African patients. The NGP analysis on this diverse dataset revealed characteristic performance differences depending on the composition of training and test sets corresponding to genetic relatedness. For clinical use of NGP, incorporating non-European patients resulted in a profound enhancement of GestaltMatcher performance. The top-5 accuracy rate increased by +11.29%. Importantly, this improvement in delineating the correct disorder from a facial portrait was achieved without decreasing the performance on European patients. By design, GMDB complies with the FAIR principles by rendering the curated medical data findable, accessible, interoperable, and reusable. This means GMDB can also serve as data for training and benchmarking. In summary, our study on facial dysmorphism on a global sample revealed a considerable cross ancestral phenotypic variability confounding NGP that should be counteracted by international efforts for increasing data diversity. GMDB will serve as a vital reference database for clinicians and a transparent training set for advancing NGP technology.

Exome copy number variant detection, analysis, and classification in a large cohort of families with undiagnosed rare genetic disease

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and, with new innovative methods, can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the Genomics Research to Elucidate the Genetics of Rare Diseases consortium and analyzed using the seqr platform. The addition of CNV detection to exome analysis identified causal CNVs for 171 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb. The causal CNVs consisted of 140 deletions, 15 duplications, 3 suspected complex structural variants (SVs), 3 insertions, and 10 complex SVs, the latter two groups being identified by orthogonal confirmation methods. To classify CNV variant pathogenicity, we used the 2020 American College of Medical Genetics and Genomics/ClinGen CNV interpretation standards and developed additional criteria to evaluate allelic and functional data as well as variants on the X chromosome to further advance the framework. We interpreted 151 CNVs as likely pathogenic/pathogenic and 20 CNVs as high-interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher-resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

Genome and RNA sequencing boost neuromuscular diagnoses to 62% from 34% with exome sequencing alone

OBJECTIVE

Most families with heritable neuromuscular disorders do not receive a molecular diagnosis. Here we evaluate diagnostic utility of exome, genome, RNA sequencing, and protein studies and provide evidence-based recommendations for their integration into practice.

METHODS

In total, 247 families with suspected monogenic neuromuscular disorders who remained without a genetic diagnosis after standard diagnostic investigations underwent research-led massively parallel sequencing: neuromuscular disorder gene panel, exome, genome, and/or RNA sequencing to identify causal variants. Protein and RNA studies were also deployed when required.

RESULTS

Integration of exome sequencing and auxiliary genome, RNA and/or protein studies identified causal or likely causal variants in 62% (152 out of 247) of families. Exome sequencing alone informed 55% (83 out of 152) of diagnoses, with remaining diagnoses (45%; 69 out of 152) requiring genome sequencing, RNA and/or protein studies to identify variants and/or support pathogenicity. Arrestingly, novel disease genes accounted for <4% (6 out of 152) of diagnoses while 36.2% of solved families (55 out of 152) harbored at least one splice-altering or structural variant in a known neuromuscular disorder gene. We posit that contemporary neuromuscular disorder gene-panel sequencing could likely provide 66% (100 out of 152) of our diagnoses today.

INTERPRETATION

Our results emphasize thorough clinical phenotyping to enable deep scrutiny of all rare genetic variation in phenotypically consistent genes. Post-exome auxiliary investigations extended our diagnostic yield by 81% overall (34-62%). We present a diagnostic algorithm that details deployment of genomic and auxiliary investigations to obtain these diagnoses today most effectively. We hope this provides a practical guide for clinicians as they gain greater access to clinical genome and transcriptome sequencing.

High number of candidate gene variants are identified as disease-causing in a period of 4 years

Advances in bioinformatic tools paired with the ongoing accumulation of genetic knowledge and periodic reanalysis of genomic sequencing data have led to an improvement in genetic diagnostic rates. Candidate gene variants (CGVs) identified during sequencing or on reanalysis but not yet implicated in human disease or associated with a phenotypically distinct condition are often not revisited, leading to missed diagnostic opportunities. Here, we revisited 33 such CGVs from our previously published study and determined that 16 of them are indeed disease-causing (novel or phenotype expansion) since their identification. These results emphasize the need to focus on previously identified CGVs during sequencing or reanalysis and the importance of sharing that information with researchers around the world, including relevant functional analysis to establish disease causality.

Balancing the safeguarding of privacy and data sharing: perceptions of genomic professionals on patient genomic data ownership in Australia

There are inherent complexities and tensions in achieving a responsible balance between safeguarding patients' privacy and sharing genomic data for advancing health and medical science. A growing body of literature suggests establishing patient genomic data ownership, enabled by blockchain technology, as one approach for managing these priorities. We conducted an online survey, applying a mixed methods approach to collect quantitative (using scale questions) and qualitative data (using open-ended questions). We explored the views of 117 genomic professionals (clinical geneticists, genetic counsellors, bioinformaticians, and researchers) towards patient data ownership in Australia. Data analysis revealed most professionals agreed that patients have rights to data ownership. However, there is a need for a clearer understanding of the nature and implications of data ownership in this context as genomic data often is subject to collective ownership (e.g., with family members and laboratories). This research finds that while the majority of genomic professionals acknowledge the desire for patient data ownership, bioinformaticians and researchers expressed more favourable views than clinical geneticists and genetic counsellors, suggesting that their views on this issue may be shaped by how closely they interact with patients as part of their professional duties. This research also confirms that stronger health system infrastructure is a prerequisite for enabling patient data ownership, which needs to be underpinned by appropriate digital infrastructure (e.g., central vs. decentralised data storage), patient identity ownership (e.g., limited vs. self-sovereign identity), and policy at both federal and state levels.

Molecular diagnosis, clinical evaluation and phenotypic spectrum of Townes-Brocks syndrome: insights from a large Chinese hearing loss cohort

BACKGROUND

Townes-Brocks syndrome (TBS) is a rare genetic disorder characterised by multiple malformations. Due to its phenotypic heterogeneity and rarity, diagnosis and recognition of TBS can be challenging and there has been a lack of investigation of patients with atypical TBS in large cohorts and delineation of their phenotypic characteristics.

METHODS

We screened SALL1 and DACT1 variants using next-generation sequencing in the China Deafness Genetics Consortium (CDGC) cohort enrolling 20 666 unrelated hearing loss (HL) cases. Comprehensive clinical evaluations were conducted on seven members from a three-generation TBS family. Combining data from previously reported cases, we also provided a landscape of phenotypes and genotypes of patients with TBS.

RESULTS

We identified five novel and two reported pathogenic/likely pathogenic (P/LP) SALL1 variants from seven families. Audiological features in patients differed in severity and binaural asymmetry. Moreover, previously undocumented malformations in the middle and inner ear were detected in one patient. By comprehensive clinical evaluations, we further provide evidence for the causal relationship between SALL1 variation and certain endocrine abnormalities. Penetrance analysis within familial contexts revealed incomplete penetrance among first-generation patients with TBS and a higher disease burden among their affected offspring.

CONCLUSION

This study presents the first insight of genetic screening for patients with TBS in a large HL cohort. We broadened the phenotypic-genotypic spectrum of TBS and our results supported an underestimated prevalence of TBS. Due to the rarity and phenotypic heterogeneity of rare diseases, broader spectrum molecular tests, especially whole genome sequencing, can improve the situation of underdiagnosis and provide effective recommendations for clinical management.

Standards of NGS Data Sharing and Analysis in Ataxias: Recommendations by the NGS Working Group of the Ataxia Global Initiative

The Ataxia Global Initiative (AGI) is a worldwide multi-stakeholder research platform to systematically enhance trial-readiness in degenerative ataxias. The next-generation sequencing (NGS) working group of the AGI aims to improve methods, platforms, and international standards for ataxia NGS analysis and data sharing, ultimately allowing to increase the number of genetically ataxia patients amenable for natural history and treatment trials. Despite extensive implementation of NGS for ataxia patients in clinical and research settings, the diagnostic gap remains sizeable, as approximately 50% of patients with hereditary ataxia remain genetically undiagnosed. One current shortcoming is the fragmentation of patients and NGS datasets on different analysis platforms and databases around the world. The AGI NGS working group in collaboration with the AGI associated research platforms-CAGC, GENESIS, and RD-Connect GPAP-provides clinicians and scientists access to user-friendly and adaptable interfaces to analyze genome-scale patient data. These platforms also foster collaboration within the ataxia community. These efforts and tools have led to the diagnosis of > 500 ataxia patients and the discovery of > 30 novel ataxia genes. Here, the AGI NGS working group presents their consensus recommendations for NGS data sharing initiatives in the ataxia field, focusing on harmonized NGS variant analysis and standardized clinical and metadata collection, combined with collaborative data and analysis tool sharing across platforms.

Unbiased phenotype and genotype matching maximizes gene discovery and diagnostic yield

PURPOSE

Widespread application of next-generation sequencing, combined with data exchange platforms, has provided molecular diagnoses for countless families. To maximize diagnostic yield, we implemented an unbiased semi-automated genematching algorithm based on genotype and phenotype matching.

METHODS

Rare homozygous variants identified in 2 or more affected individuals, but not in healthy individuals, were extracted from our local database of ∼12,000 exomes. Phenotype similarity scores (PSS), based on human phenotype ontology terms, were assigned to each pair of individuals matched at the genotype level using HPOsim.

RESULTS

33,792 genotype-matched pairs were discovered, representing variants in 7567 unique genes. There was an enrichment of PSS ≥0.1 among pathogenic/likely pathogenic variant-level pairs (94.3% in pathogenic/likely pathogenic variant-level matches vs 34.75% in all matches). We highlighted founder or region-specific variants as an internal positive control and proceeded to identify candidate disease genes. Variant-level matches were particularly helpful in cases involving inframe indels and splice region variants beyond the canonical splice sites, which may otherwise have been disregarded, allowing for detection of candidate disease genes, such as KAT2A, RPAIN, and LAMP3.

CONCLUSION

Semi-automated genotype matching combined with PSS is a powerful tool to resolve variants of uncertain significance and to identify candidate disease genes.

Best practices for multimodal clinical data management and integration: An atopic dermatitis research case

BACKGROUND

In clinical research on multifactorial diseases such as atopic dermatitis, data-driven medical research has become more widely used as means to clarify diverse pathological conditions and to realize precision medicine. However, modern clinical data, characterized as large-scale, multimodal, and multi-center, causes difficulties in data integration and management, which limits productivity in clinical data science.

METHODS

We designed a generic data management flow to collect, cleanse, and integrate data to handle different types of data generated at multiple institutions by 10 types of clinical studies. We developed MeDIA (Medical Data Integration Assistant), a software to browse the data in an integrated manner and extract subsets for analysis.

RESULTS

MeDIA integrates and visualizes data and information on research participants obtained from multiple studies. It then provides a sophisticated interface that supports data management and helps data scientists retrieve the data sets they need. Furthermore, the system promotes the use of unified terms such as identifiers or sampling dates to reduce the cost of pre-processing by data analysts. We also propose best practices in clinical data management flow, which we learned from the development and implementation of MeDIA.

CONCLUSIONS

The MeDIA system solves the problem of multimodal clinical data integration, from complex text data such as medical records to big data such as omics data from a large number of patients. The system and the proposed best practices can be applied not only to allergic diseases but also to other diseases to promote data-driven medical research.

Expansion of the Genotypic and Phenotypic Spectrum of ASH1L-Related Syndromic Neurodevelopmental Disorder

Pathogenic ASH1L variants have been reported in probands with broad phenotypic presentations, including intellectual disability, autism spectrum disorder, attention deficit hyperactivity disorder, seizures, congenital anomalies, and other skeletal, muscular, and sleep differences. Here, we review previously published individuals with pathogenic ASH1L variants and report three further probands with novel ASH1L variants and previously unreported phenotypic features, including mixed receptive language disorder and gait disturbances. These novel data from the Brain Gene Registry, an accessible repository of clinically derived genotypic and phenotypic data, have allowed for the expansion of the phenotypic and genotypic spectrum of this condition.

Long-read genome sequencing and variant reanalysis increase diagnostic yield in neurodevelopmental disorders

Variant detection from long-read genome sequencing (lrGS) has proven to be considerably more accurate and comprehensive than variant detection from short-read genome sequencing (srGS). However, the rate at which lrGS can increase molecular diagnostic yield for rare disease is not yet precisely characterized. We performed lrGS using Pacific Biosciences "HiFi" technology on 96 short-read-negative probands with rare disease that were suspected to be genetic. We generated hg38-aligned variants and de novo phased genome assemblies, and subsequently annotated, filtered, and curated variants using clinical standards. New disease-relevant or potentially relevant genetic findings were identified in 16/96 (16.7%) probands, eight of which (8/96, 8.33%) harbored pathogenic or likely pathogenic variants. Newly identified variants were visible in both srGS and lrGS in nine probands (~9.4%) and resulted from changes to interpretation mostly from recent gene-disease association discoveries. Seven cases included variants that were only interpretable in lrGS, including copy-number variants, an inversion, a mobile element insertion, two low-complexity repeat expansions, and a 1 bp deletion. While evidence for each of these variants is, in retrospect, visible in srGS, they were either: not called within srGS data, were represented by calls with incorrect sizes or structures, or failed quality-control and filtration. Thus, while reanalysis of older data clearly increases diagnostic yield, we find that lrGS allows for substantial additional yield (7/96, 7.3%) beyond srGS. We anticipate that as lrGS analysis improves, and as lrGS datasets grow allowing for better variant frequency annotation, the additional lrGS-only rare disease yield will grow over time.

Recommendations for laboratory workflow that better support centralised amalgamation of genomic variant data: findings from CanVIG-UK national molecular laboratory survey

BACKGROUND

National and international amalgamation of genomic data offers opportunity for research and audit, including analyses enabling improved classification of variants of uncertain significance. Review of individual-level data from National Health Service (NHS) testing of cancer susceptibility genes (2002-2023) submitted to the National Disease Registration Service revealed heterogeneity across participating laboratories regarding (1) the structure, quality and completeness of submitted data, and (2) the ease with which that data could be assembled locally for submission.

METHODS

In May 2023, we undertook a closed online survey of 51 clinical scientists who provided consensus responses representing all 17 of 17 NHS molecular genetic laboratories in England and Wales which undertake NHS diagnostic analyses of cancer susceptibility genes. The survey included 18 questions relating to 'next-generation sequencing workflow' (11), 'variant classification' (3) and 'phenotypical context' (4).

RESULTS

Widely differing processes were reported for transfer of variant data into their local LIMS (Laboratory Information Management System), for the formatting in which the variants are stored in the LIMS and which classes of variants are retained in the local LIMS. Differing local provisions and workflow for variant classifications were also reported, including the resources provided and the mechanisms by which classifications are stored.

CONCLUSION

The survey responses illustrate heterogeneous laboratory workflow for preparation of genomic variant data from local LIMS for centralised submission. Workflow is often labour-intensive and inefficient, involving multiple manual steps which introduce opportunities for error. These survey findings and adoption of the concomitant recommendations may support improvement in laboratory dataflows, better facilitating submission of data for central amalgamation.

Discovery of novel genetic syndromes in Latin America: Opportunities and challenges

Latin America (LatAm) has a rich and historically significant role in delineating both novel and well-documented genetic disorders. However, the ongoing advancements in the field of human genetics pose challenges to the relatively slow adaption of LatAm in the field. Here, we describe past and present contributions of LatAm to the discovery of novel genetic disorders, often referred as novel gene-disease associations (NGDA). We also describe the current methodologies for discovery of NGDA, taking into account the latest developments in genomics. We provide an overview of opportunities and challenges for NGDA research in LatAm considering the steps currently performed to identify and validate such associations. Given the multiple and diverse needs of populations and countries in LatAm, it is imperative to foster collaborations amongst patients, indigenous people, clinicians and scientists. Such collaborative effort is essential for sustaining and enhancing the LatAm´s contributions to the field of NGDA.

Neurodevelopmental disorders associated variants in ADAT3 disrupt the activity of the ADAT2/ADAT3 tRNA deaminase complex and impair neuronal migration

The ADAT2/ADAT3 complex catalyzes the adenosine to inosine modification at the wobble position of eukaryotic tRNAs. Mutations in ADAT3 , the catalytically inactive subunit of the ADAT2/ADAT3 complex, have been identified in patients presenting with severe neurodevelopmental disorders (NDDs). Yet, the physiological function of ADAT2/ADAT3 complex during brain development remains totally unknown. Here we showed that maintaining a proper level of ADAT2/ADAT3 catalytic activity is required for correct radial migration of projection neurons in the developing mouse cortex. In addition, we not only reported 7 new NDD patients carrying biallelic variants in ADAT3 but also deeply characterize the impact of those variants on ADAT2/ADAT3 structure, biochemical properties, enzymatic activity and tRNAs editing and abundance. We demonstrated that all the identified variants alter both the expression and the activity of the complex leading to a significant decrease of I 34 with direct consequence on their steady-state. Using in vivo complementation assays, we correlated the severity of the migration phenotype with the degree of the loss of function caused by the variants. Altogether, our results indicate a critical role of ADAT2/ADAT3 during cortical development and provide cellular and molecular insights into the pathogenicity of ADAT3-related neurodevelopmental disorder.

Slice Testing-Considerations from Ordering to Reporting: A Joint Report of the Association for Molecular Pathology, College of American Pathologists, and National Society of Genetic Counselors

As the number of genes associated with various germline disorders continues to grow, it is becoming more difficult for clinical laboratories to maintain separate assays for interrogating disease-focused gene panels. One solution to this challenge is termed slice testing, where capture backbone is used to analyze data specific to a set of genes, and for this article, we will focus on exome. A key advantage to this strategy is greater flexibility by adding genes as they become associated with disease or the ability to accommodate specific provider requests. Here, we provide expert consensus recommendations and results from an Association for Molecular Pathology-sponsored survey of clinical laboratories performing exome sequencing to compare a slice testing approach with traditional static gene panels and comprehensive exome analysis. We explore specific considerations for slices, including gene selection, analytic performance, coverage, quality, and interpretation. Our goal is to provide comprehensive guidance for clinical laboratories interested in designing and using slice tests as a diagnostic.

Heterozygous MAP3K20 variants cause ectodermal dysplasia, craniosynostosis, sensorineural hearing loss, and limb anomalies

Biallelic pathogenic variants in MAP3K20, which encodes a mitogen-activated protein kinase, are a rare cause of split-hand foot malformation (SHFM), hearing loss, and nail abnormalities or congenital myopathy. However, heterozygous variants in this gene have not been definitively associated with a phenotype. Here, we describe the phenotypic spectrum associated with heterozygous de novo variants in the linker region between the kinase domain and leucine zipper domain of MAP3K20. We report five individuals with diverse clinical features, including craniosynostosis, limb anomalies, sensorineural hearing loss, and ectodermal dysplasia-like phenotypes who have heterozygous de novo variants in this specific region of the gene. These individuals exhibit both shared and unique clinical manifestations, highlighting the complexity and variability of the disorder. We propose that the involvement of MAP3K20 in endothelial-mesenchymal transition provides a plausible etiology of these features. Together, these findings characterize a disorder that both expands the phenotypic spectrum associated with MAP3K20 and highlights the need for further studies on its role in early human development.

GPAD: a natural language processing-based application to extract the gene-disease association discovery information from OMIM

BACKGROUND

Thousands of genes have been associated with different Mendelian conditions. One of the valuable sources to track these gene-disease associations (GDAs) is the Online Mendelian Inheritance in Man (OMIM) database. However, most of the information in OMIM is textual, and heterogeneous (e.g. summarized by different experts), which complicates automated reading and understanding of the data. Here, we used Natural Language Processing (NLP) to make a tool (Gene-Phenotype Association Discovery (GPAD)) that could syntactically process OMIM text and extract the data of interest.

RESULTS

GPAD applies a series of language-based techniques to the text obtained from OMIM API to extract GDA discovery-related information. GPAD can inform when a particular gene was associated with a specific phenotype, as well as the type of validation-whether through model organisms or cohort-based patient-matching approaches-for such an association. GPAD extracted data was validated with published reports and was compared with large language model. Utilizing GPAD's extracted data, we analysed trends in GDA discoveries, noting a significant increase in their rate after the introduction of exome sequencing, rising from an average of about 150-250 discoveries each year. Contrary to hopes of resolving most GDAs for Mendelian disorders by now, our data indicate a substantial decline in discovery rates over the past five years (2017-2022). This decline appears to be linked to the increasing necessity for larger cohorts to substantiate GDAs. The rising use of zebrafish and Drosophila as model organisms in providing evidential support for GDAs is also observed.

CONCLUSIONS

GPAD's real-time analyzing capacity offers an up-to-date view of GDA discovery and could help in planning and managing the research strategies. In future, this solution can be extended or modified to capture other information in OMIM and scientific literature.

Novel syndromic neurodevelopmental disorder caused by de novo deletion of CHASERR, a long noncoding RNA

Genes encoding long non-coding RNAs (lncRNAs) comprise a large fraction of the human genome, yet haploinsufficiency of a lncRNA has not been shown to cause a Mendelian disease. CHASERR is a highly conserved human lncRNA adjacent to CHD2-a coding gene in which de novo loss-of-function variants cause developmental and epileptic encephalopathy. Here we report three unrelated individuals each harboring an ultra-rare heterozygous de novo deletion in the CHASERR locus. We report similarities in severe developmental delay, facial dysmorphisms, and cerebral dysmyelination in these individuals, distinguishing them from the phenotypic spectrum of CHD2 haploinsufficiency. We demonstrate reduced CHASERR mRNA expression and corresponding increased CHD2 mRNA and protein in whole blood and patient-derived cell lines-specifically increased expression of the CHD2 allele in cis with the CHASERR deletion, as predicted from a prior mouse model of Chaserr haploinsufficiency. We show for the first time that de novo structural variants facilitated by Alu-mediated non-allelic homologous recombination led to deletion of a non-coding element (the lncRNA CHASERR) to cause a rare syndromic neurodevelopmental disorder. We also demonstrate that CHD2 has bidirectional dosage sensitivity in human disease. This work highlights the need to carefully evaluate other lncRNAs, particularly those upstream of genes associated with Mendelian disorders.

Next-generation sequencing and bioinformatics in rare movement disorders

The ability to sequence entire exomes and genomes has revolutionized molecular testing in rare movement disorders, and genomic sequencing is becoming an integral part of routine diagnostic workflows for these heterogeneous conditions. However, interpretation of the extensive genomic variant information that is being generated presents substantial challenges. In this Perspective, we outline multidimensional strategies for genetic diagnosis in patients with rare movement disorders. We examine bioinformatics tools and computational metrics that have been developed to facilitate accurate prioritization of disease-causing variants. Additionally, we highlight community-driven data-sharing and case-matchmaking platforms, which are designed to foster the discovery of new genotype-phenotype relationships. Finally, we consider how multiomic data integration might optimize diagnostic success by combining genomic, epigenetic, transcriptomic and/or proteomic profiling to enable a more holistic evaluation of variant effects. Together, the approaches that we discuss offer pathways to the improved understanding of the genetic basis of rare movement disorders.

Whole genome sequencing in clinical practice

Whole genome sequencing (WGS) is becoming the preferred method for molecular genetic diagnosis of rare and unknown diseases and for identification of actionable cancer drivers. Compared to other molecular genetic methods, WGS captures most genomic variation and eliminates the need for sequential genetic testing. Whereas, the laboratory requirements are similar to conventional molecular genetics, the amount of data is large and WGS requires a comprehensive computational and storage infrastructure in order to facilitate data processing within a clinically relevant timeframe. The output of a single WGS analyses is roughly 5 MIO variants and data interpretation involves specialized staff collaborating with the clinical specialists in order to provide standard of care reports. Although the field is continuously refining the standards for variant classification, there are still unresolved issues associated with the clinical application. The review provides an overview of WGS in clinical practice - describing the technology and current applications as well as challenges connected with data processing, interpretation and clinical reporting.

Variants in the WDR44 WD40-repeat domain cause a spectrum of ciliopathy by impairing ciliogenesis initiation

WDR44 prevents ciliogenesis initiation by regulating RAB11-dependent vesicle trafficking. Here, we describe male patients with missense and nonsense variants within the WD40 repeats (WDR) of WDR44, an X-linked gene product, who display ciliopathy-related developmental phenotypes that we can model in zebrafish. The patient phenotypic spectrum includes developmental delay/intellectual disability, hypotonia, distinct craniofacial features and variable presence of brain, renal, cardiac and musculoskeletal abnormalities. We demonstrate that WDR44 variants associated with more severe disease impair ciliogenesis initiation and ciliary signaling. Because WDR44 negatively regulates ciliogenesis, it was surprising that pathogenic missense variants showed reduced abundance, which we link to misfolding of WDR autonomous repeats and degradation by the proteasome. We discover that disease severity correlates with increased RAB11 binding, which we propose drives ciliogenesis initiation dysregulation. Finally, we discover interdomain interactions between the WDR and NH2-terminal region that contains the RAB11 binding domain (RBD) and show patient variants disrupt this association. This study provides new insights into WDR44 WDR structure and characterizes a new syndrome that could result from impaired ciliogenesis.

The Human Phenotype Ontology in 2024: phenotypes around the world

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

A syndromic neurodevelopmental disorder caused by rare variants in PPFIA3

PPFIA3 encodes the protein-tyrosine phosphatase, receptor-type, F-polypeptide-interacting-protein-alpha-3 (PPFIA3), which is a member of the LAR-protein-tyrosine phosphatase-interacting-protein (liprin) family involved in synapse formation and function, synaptic vesicle transport, and presynaptic active zone assembly. The protein structure and function are evolutionarily well conserved, but human diseases related to PPFIA3 dysfunction are not yet reported in OMIM. Here, we report 20 individuals with rare PPFIA3 variants (19 heterozygous and 1 compound heterozygous) presenting with developmental delay, intellectual disability, hypotonia, dysmorphisms, microcephaly or macrocephaly, autistic features, and epilepsy with reduced penetrance. Seventeen unique PPFIA3 variants were detected in 18 families. To determine the pathogenicity of PPFIA3 variants in vivo, we generated transgenic fruit flies producing either human wild-type (WT) PPFIA3 or five missense variants using GAL4-UAS targeted gene expression systems. In the fly overexpression assays, we found that the PPFIA3 variants in the region encoding the N-terminal coiled-coil domain exhibited stronger phenotypes compared to those affecting the C-terminal region. In the loss-of-function fly assay, we show that the homozygous loss of fly Liprin-α leads to embryonic lethality. This lethality is partially rescued by the expression of human PPFIA3 WT, suggesting human PPFIA3 function is partially conserved in the fly. However, two of the tested variants failed to rescue the lethality at the larval stage and one variant failed to rescue lethality at the adult stage. Altogether, the human and fruit fly data reveal that the rare PPFIA3 variants are dominant-negative loss-of-function alleles that perturb multiple developmental processes and synapse formation.

Spliceosome malfunction causes neurodevelopmental disorders with overlapping features

Pre-mRNA splicing is a highly coordinated process. While its dysregulation has been linked to neurological deficits, our understanding of the underlying molecular and cellular mechanisms remains limited. We implicated pathogenic variants in U2AF2 and PRPF19, encoding spliceosome subunits in neurodevelopmental disorders (NDDs), by identifying 46 unrelated individuals with 23 de novo U2AF2 missense variants (including 7 recurrent variants in 30 individuals) and 6 individuals with de novo PRPF19 variants. Eight U2AF2 variants dysregulated splicing of a model substrate. Neuritogenesis was reduced in human neurons differentiated from human pluripotent stem cells carrying two U2AF2 hyper-recurrent variants. Neural loss of function (LoF) of the Drosophila orthologs U2af50 and Prp19 led to lethality, abnormal mushroom body (MB) patterning, and social deficits, which were differentially rescued by wild-type and mutant U2AF2 or PRPF19. Transcriptome profiling revealed splicing substrates or effectors (including Rbfox1, a third splicing factor), which rescued MB defects in U2af50-deficient flies. Upon reanalysis of negative clinical exomes followed by data sharing, we further identified 6 patients with NDD who carried RBFOX1 missense variants which, by in vitro testing, showed LoF. Our study implicates 3 splicing factors as NDD-causative genes and establishes a genetic network with hierarchy underlying human brain development and function.

A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders

Unsolved Mendelian cases often lack obvious pathogenic coding variants, suggesting potential non-coding etiologies. Here, we present a single cell multi-omic framework integrating embryonic mouse chromatin accessibility, histone modification, and gene expression assays to discover cranial motor neuron (cMN) cis-regulatory elements and subsequently nominate candidate non-coding variants in the congenital cranial dysinnervation disorders (CCDDs), a set of Mendelian disorders altering cMN development. We generated single cell epigenomic profiles for ~86,000 cMNs and related cell types, identifying ~250,000 accessible regulatory elements with cognate gene predictions for ~145,000 putative enhancers. Seventy-five percent of elements (44 of 59) validated in an in vivo transgenic reporter assay, demonstrating that single cell accessibility is a strong predictor of enhancer activity. Applying our cMN atlas to 899 whole genome sequences from 270 genetically unsolved CCDD pedigrees, we achieved significant reduction in our variant search space and nominated candidate variants predicted to regulate known CCDD disease genes MAFB, PHOX2A, CHN1, and EBF3 - as well as new candidates in recurrently mutated enhancers through peak- and gene-centric allelic aggregation. This work provides novel non-coding variant discoveries of relevance to CCDDs and a generalizable framework for nominating non-coding variants of potentially high functional impact in other Mendelian disorders.

A deep learning transformer model predicts high rates of undiagnosed rare disease in large electronic health systems

It is estimated that as many as 1 in 16 people worldwide suffer from rare diseases. Rare disease patients face difficulty finding diagnosis and treatment for their conditions, including long diagnostic odysseys, multiple incorrect diagnoses, and unavailable or prohibitively expensive treatments. As a result, it is likely that large electronic health record (EHR) systems include high numbers of participants suffering from undiagnosed rare disease. While this has been shown in detail for specific diseases, these studies are expensive and time consuming and have only been feasible to perform for a handful of the thousands of known rare diseases. The bulk of these undiagnosed cases are effectively hidden, with no straightforward way to differentiate them from healthy controls. The ability to access them at scale would enormously expand our capacity to study and develop drugs for rare diseases, adding to tools aimed at increasing availability of study cohorts for rare disease. In this study, we train a deep learning transformer algorithm, RarePT (Rare-Phenotype Prediction Transformer), to impute undiagnosed rare disease from EHR diagnosis codes in 436,407 participants in the UK Biobank and validated on an independent cohort from 3,333,560 individuals from the Mount Sinai Health System. We applied our model to 155 rare diagnosis codes with fewer than 250 cases each in the UK Biobank and predicted participants with elevated risk for each diagnosis, with the number of participants predicted to be at risk ranging from 85 to 22,000 for different diagnoses. These risk predictions are significantly associated with increased mortality for 65% of diagnoses, with disease burden expressed as disability-adjusted life years (DALY) for 73% of diagnoses, and with 72% of available disease-specific diagnostic tests. They are also highly enriched for known rare diagnoses in patients not included in the training set, with an odds ratio (OR) of 48.0 in cross-validation cohorts of the UK Biobank and an OR of 30.6 in the independent Mount Sinai Health System cohort. Most importantly, RarePT successfully screens for undiagnosed patients in 32 rare diseases with available diagnostic tests in the UK Biobank. Using the trained model to estimate the prevalence of undiagnosed disease in the UK Biobank for these 32 rare phenotypes, we find that at least 50% of patients remain undiagnosed for 20 of 32 diseases. These estimates provide empirical evidence of a high prevalence of undiagnosed rare disease, as well as demonstrating the enormous potential benefit of using RarePT to screen for undiagnosed rare disease patients in large electronic health systems.

A Panel-Agnostic Strategy 'HiPPo' Improves Diagnostic Efficiency in the UK Genomic Medicine Service

Genome sequencing is available as a clinical test in the UK through the Genomic Medicine Service (GMS). The GMS analytical strategy predominantly filters genome data on preselected gene panels. Whilst this reduces variants requiring assessment by reporting laboratories, pathogenic variants outside applied panels may be missed, and variants in genes without established disease-gene relationships are largely ignored. This study compares the analysis of a research exome to a GMS clinical genome for the same patients. For the research exome, we applied a panel-agnostic approach filtering for variants with High Pathogenic Potential (HiPPo) using ClinVar, allele frequency, and in silico prediction tools. We then restricted HiPPo variants to Gene Curation Coalition (GenCC) disease genes. These results were compared with the GMS genome panel-based approach. Twenty-four participants from eight families underwent parallel research exome and GMS genome sequencing. Exome HiPPo analysis identified a similar number of variants as the GMS panel-based approach. GMS genome analysis returned two pathogenic variants and one de novo variant. Exome HiPPo analysis returned the same variants plus an additional pathogenic variant and three further de novo variants in novel genes, where case series are underway. When HiPPo was restricted to GenCC disease genes, statistically fewer variants required assessment to identify more pathogenic variants than reported by the GMS, giving a diagnostic rate per variant assessed of 20% for HiPPo versus 3% for the GMS. With UK plans to sequence 5 million genomes, strategies are needed to optimise genome analysis beyond gene panels whilst minimising the burden of variants requiring clinical assessment.

Exome copy number variant detection, analysis and classification in a large cohort of families with undiagnosed rare genetic disease

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and with new innovative methods can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the GREGoR consortium. Each family's CNV data was analyzed using the seqr platform and candidate CNVs classified using the 2020 ACMG/ClinGen CNV interpretation standards. We developed additional evidence criteria to address situations not covered by the current standards. The addition of CNV calling to exome analysis identified causal CNVs for 173 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb with estimates that 44% would not have been detected by standard chromosomal microarrays. The causal CNVs consisted of 141 deletions, 15 duplications, 4 suspected complex structural variants (SVs), 3 insertions and 10 complex SVs, the latter two groups being identified by orthogonal validation methods. We interpreted 153 CNVs as likely pathogenic/pathogenic and 20 CNVs as high interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

Precision medicine in rare diseases: What is next?

Molecular diagnostics is a cornerstone of modern precision medicine, broadly understood as tailoring an individual's treatment, follow-up, and care based on molecular data. In rare diseases (RDs), molecular diagnoses reveal valuable information about the cause of symptoms, disease progression, familial risk, and in certain cases, unlock access to targeted therapies. Due to decreasing DNA sequencing costs, genome sequencing (GS) is emerging as the primary method for precision diagnostics in RDs. Several ongoing European initiatives for precision medicine have chosen GS as their method of choice. Recent research supports the role for GS as first-line genetic investigation in individuals with suspected RD, due to its improved diagnostic yield compared to other methods. Moreover, GS can detect a broad range of genetic aberrations including those in noncoding regions, producing comprehensive data that can be periodically reanalyzed for years to come when further evidence emerges. Indeed, targeted drug development and repurposing of medicines can be accelerated as more individuals with RDs receive a molecular diagnosis. Multidisciplinary teams in which clinical specialists collaborate with geneticists, genomics education of professionals and the public, and dialogue with patient advocacy groups are essential elements for the integration of precision medicine into clinical practice worldwide. It is also paramount that large research projects share genetic data and leverage novel technologies to fully diagnose individuals with RDs. In conclusion, GS increases diagnostic yields and is a crucial step toward precision medicine for RDs. Its clinical implementation will enable better patient management, unlock targeted therapies, and guide the development of innovative treatments.

Pathogenic variants in SMARCA1 cause an X-linked neurodevelopmental disorder modulated by NURF complex composition

Pathogenic variants in ATP-dependent chromatin remodeling proteins are a recurrent cause of neurodevelopmental disorders (NDDs). The NURF complex consists of BPTF and either the SNF2H (SMARCA5) or SNF2L (SMARCA1) ISWI-chromatin remodeling enzyme. Pathogenic variants in BPTF and SMARCA5 were previously implicated in NDDs. Here, we describe 40 individuals from 30 families with de novo or maternally inherited pathogenic variants in SMARCA1. This novel NDD was associated with mild to severe ID/DD, delayed or regressive speech development, and some recurrent facial dysmorphisms. Individuals carrying SMARCA1 loss-of-function variants exhibited a mild genome-wide DNA methylation profile and a high penetrance of macrocephaly. Genetic dissection of the NURF complex using Smarca1, Smarca5, and Bptfsingle and double mouse knockouts revealed the importance of NURF composition and dosage for proper forebrain development. Finally, we propose that genetic alterations affecting different NURF components result in a NDD with a broad clinical spectrum.

Phenotype and genetic analysis of data collected within the first year of NeuroDev

Genetic association studies have made significant contributions to our understanding of the etiology of neurodevelopmental disorders (NDDs). However, these studies rarely focused on the African continent. The NeuroDev Project aims to address this diversity gap through detailed phenotypic and genetic characterization of children with NDDs from Kenya and South Africa. We present results from NeuroDev's first year of data collection, including phenotype data from 206 cases and clinical genetic analyses of 99 parent-child trios. Most cases met criteria for global developmental delay/intellectual disability (GDD/ID, 80.3%). Approximately half of the children with GDD/ID also met criteria for autism. Analysis of exome-sequencing data identified a pathogenic or likely pathogenic variant in 13 (17%) of the 75 cases from South Africa and 9 (38%) of the 24 cases from Kenya. Data from the trio pilot are publicly available, and the NeuroDev Project will continue to develop resources for the global genetics community.

Rare variants in ANO1, encoding a calcium-activated chloride channel, predispose to moyamoya disease

Moyamoya disease, a cerebrovascular disease leading to strokes in children and young adults, is characterized by progressive occlusion of the distal internal carotid arteries and the formation of collateral vessels. Altered genes play a prominent role in the aetiology of moyamoya disease, but a causative gene is not identified in the majority of cases. Exome sequencing data from 151 individuals from 84 unsolved families were analysed to identify further genes for moyamoya disease, then candidate genes assessed in additional cases (150 probands). Two families had the same rare variant in ANO1, which encodes a calcium-activated chloride channel, anoctamin-1. Haplotype analyses found the families were related, and ANO1 p.Met658Val segregated with moyamoya disease in the family with an LOD score of 3.3. Six additional ANO1 rare variants were identified in moyamoya disease families. The ANO1 rare variants were assessed using patch-clamp recordings, and the majority of variants, including ANO1 p.Met658Val, displayed increased sensitivity to intracellular Ca2+. Patients harbouring these gain-of-function ANO1 variants had classic features of moyamoya disease, but also had aneurysm, stenosis and/or occlusion in the posterior circulation. Our studies support that ANO1 gain-of-function pathogenic variants predispose to moyamoya disease and are associated with unique involvement of the posterior circulation.

Exome Sequencing and the Identification of New Genes and Shared Mechanisms in Polymicrogyria

Importance

Polymicrogyria is the most commonly diagnosed cortical malformation and is associated with neurodevelopmental sequelae including epilepsy, motor abnormalities, and cognitive deficits. Polymicrogyria frequently co-occurs with other brain malformations or as part of syndromic diseases. Past studies of polymicrogyria have defined heterogeneous genetic and nongenetic causes but have explained only a small fraction of cases.

Objective

To survey germline genetic causes of polymicrogyria in a large cohort and to consider novel polymicrogyria gene associations.

Design, Setting, and Participants

This genetic association study analyzed panel sequencing and exome sequencing of accrued DNA samples from a retrospective cohort of families with members with polymicrogyria. Samples were accrued over more than 20 years (1994 to 2020), and sequencing occurred in 2 stages: panel sequencing (June 2015 to January 2016) and whole-exome sequencing (September 2019 to March 2020). Individuals seen at multiple clinical sites for neurological complaints found to have polymicrogyria on neuroimaging, then referred to the research team by evaluating clinicians, were included in the study. Targeted next-generation sequencing and/or exome sequencing were performed on probands (and available parents and siblings) from 284 families with individuals who had isolated polymicrogyria or polymicrogyria as part of a clinical syndrome and no genetic diagnosis at time of referral from clinic, with sequencing from 275 families passing quality control.

Main Outcomes and Measures

The number of families in whom genetic sequencing yielded a molecular diagnosis that explained the polymicrogyria in the family. Secondarily, the relative frequency of different genetic causes of polymicrogyria and whether specific genetic causes were associated with co-occurring head size changes were also analyzed.

Results

In 32.7% (90 of 275) of polymicrogyria-affected families, genetic variants were identified that provided satisfactory molecular explanations. Known genes most frequently implicated by polymicrogyria-associated variants in this cohort were PIK3R2, TUBB2B, COL4A1, and SCN3A. Six candidate novel polymicrogyria genes were identified or confirmed: de novo missense variants in PANX1, QRICH1, and SCN2A and compound heterozygous variants in TMEM161B, KIF26A, and MAN2C1, each with consistent genotype-phenotype relationships in multiple families.

Conclusions and Relevance

This study's findings reveal a higher than previously recognized rate of identifiable genetic causes, specifically of channelopathies, in individuals with polymicrogyria and support the utility of exome sequencing for families affected with polymicrogyria.

DECIPHER: Improving Genetic Diagnosis Through Dynamic Integration of Genomic and Clinical Data

DECIPHER (Database of Genomic Variation and Phenotype in Humans Using Ensembl Resources) shares candidate diagnostic variants and phenotypic data from patients with genetic disorders to facilitate research and improve the diagnosis, management, and therapy of rare diseases. The platform sits at the boundary between genomic research and the clinical community. DECIPHER aims to ensure that the most up-to-date data are made rapidly available within its interpretation interfaces to improve clinical care. Newly integrated cardiac case-control data that provide evidence of gene-disease associations and inform variant interpretation exemplify this mission. New research resources are presented in a format optimized for use by a broad range of professionals supporting the delivery of genomic medicine. The interfaces within DECIPHER integrate and contextualize variant and phenotypic data, helping to determine a robust clinico-molecular diagnosis for rare-disease patients, which combines both variant classification and clinical fit. DECIPHER supports discovery research, connecting individuals within the rare-disease community to pursue hypothesis-driven research.

Role of Incentives in the Use of Blockchain-Based Platforms for Sharing Sensitive Health Data: Experimental Study

BACKGROUND

Blockchain is an emerging technology that enables secure and decentralized approaches to reduce technical risks and governance challenges associated with sharing data. Although blockchain-based solutions have been suggested for sharing health information, it is still unclear whether a suitable incentive mechanism (intrinsic or extrinsic) can be identified to encourage individuals to share their sensitive data for research purposes.

OBJECTIVE

This study aimed to investigate how important extrinsic incentives are and what type of incentive is the best option in blockchain-based platforms designed for sharing sensitive health information.

METHODS

In this study, we conducted 3 experiments with 493 individuals to investigate the role of extrinsic incentives (ie, cryptocurrency, money, and recognition) in data sharing with research organizations.

RESULTS

The findings highlight that offering different incentives is insufficient to encourage individuals to use blockchain technology or to change their perceptions about the technology's premise for sharing sensitive health data. The results demonstrate that individuals still attribute serious risks to blockchain-based platforms. Privacy and security concerns, trust issues, lack of knowledge about the technology, lack of public acceptance, and lack of regulations are reported as top risks. In terms of attracting people to use blockchain-based platforms for data sharing in health care, we show that the effects of extrinsic motivations (cryptoincentives, money, and status) are significantly overshadowed by inhibitors to technology use.

CONCLUSIONS

We suggest that before emphasizing the use of various types of extrinsic incentives, the users must be educated about the capabilities and benefits offered by this technology. Thus, an essential first step for shifting from an institution-based data exchange to a patient-centric data exchange (using blockchain) is addressing technology inhibitors to promote patient-driven data access control. This study shows that extrinsic incentives alone are inadequate to change users' perceptions, increase their trust, or encourage them to use technology for sharing health data.

De novo missense variants in phosphatidylinositol kinase PIP5KIγ underlie a neurodevelopmental syndrome associated with altered phosphoinositide signaling

Phosphoinositides (PIs) are membrane phospholipids produced through the local activity of PI kinases and phosphatases that selectively add or remove phosphate groups from the inositol head group. PIs control membrane composition and play key roles in many cellular processes including actin dynamics, endosomal trafficking, autophagy, and nuclear functions. Mutations in phosphatidylinositol 4,5 bisphosphate [PI(4,5)P2] phosphatases cause a broad spectrum of neurodevelopmental disorders such as Lowe and Joubert syndromes and congenital muscular dystrophy with cataracts and intellectual disability, which are thus associated with increased levels of PI(4,5)P2. Here, we describe a neurodevelopmental disorder associated with an increase in the production of PI(4,5)P2 and with PI-signaling dysfunction. We identified three de novo heterozygous missense variants in PIP5K1C, which encodes an isoform of the phosphatidylinositol 4-phosphate 5-kinase (PIP5KIγ), in nine unrelated children exhibiting intellectual disability, developmental delay, acquired microcephaly, seizures, visual abnormalities, and dysmorphic features. We provide evidence that the PIP5K1C variants result in an increase of the endosomal PI(4,5)P2 pool, giving rise to ectopic recruitment of filamentous actin at early endosomes (EEs) that in turn causes dysfunction in EE trafficking. In addition, we generated an in vivo zebrafish model that recapitulates the disorder we describe with developmental defects affecting the forebrain, including the eyes, as well as craniofacial abnormalities, further demonstrating the pathogenic effect of the PIP5K1C variants.

Broadening the phenotypic and molecular spectrum of FINCA syndrome: Biallelic NHLRC2 variants in 15 novel individuals

FINCA syndrome [MIM: 618278] is an autosomal recessive multisystem disorder characterized by fibrosis, neurodegeneration and cerebral angiomatosis. To date, 13 patients from nine families with biallelic NHLRC2 variants have been published. In all of them, the recurrent missense variant p.(Asp148Tyr) was detected on at least one allele. Common manifestations included lung or muscle fibrosis, respiratory distress, developmental delay, neuromuscular symptoms and seizures often followed by early death due to rapid disease progression.Here, we present 15 individuals from 12 families with an overlapping phenotype associated with nine novel NHLRC2 variants identified by exome analysis. All patients described here presented with moderate to severe global developmental delay and variable disease progression. Seizures, truncal hypotonia and movement disorders were frequently observed. Notably, we also present the first eight cases in which the recurrent p.(Asp148Tyr) variant was not detected in either homozygous or compound heterozygous state.We cloned and expressed all novel and most previously published non-truncating variants in HEK293-cells. From the results of these functional studies, we propose a potential genotype-phenotype correlation, with a greater reduction in protein expression being associated with a more severe phenotype.Taken together, our findings broaden the known phenotypic and molecular spectrum and emphasize that NHLRC2-related disease should be considered in patients presenting with intellectual disability, movement disorders, neuroregression and epilepsy with or without pulmonary involvement.